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English
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ArXiv:
Tags:
benchmark
llm-evaluation
large-language-models
large-language-model
large-multimodal-models
llm-training
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---
license: mit
task_categories:
- text2text-generation
- text-generation
- text-retrieval
- question-answering
language:
- en
tags:
- benchmark
- llm-evaluation
- large-language-models
- large-language-model
- large-multimodal-models
- llm-training
- foundation-models
- machine-learning
- deep-learning
configs:
- config_name: all_responses
data_files: "AllResponses.csv"
- config_name: clean_responses
data_files: "CleanResponses.csv"
- config_name: additional_data
data_files: "KeyQuestions.csv"
---
---
MSEval Dataset:
---
A benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design.
The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts.
This can be used to evaluate a Language model performance and its spread compared to a human evaluation.
To get into a more detailed explanation - use this link [https://arxiv.org/abs/2407.09719v1]
---
# Overview
We introduce MSEval, a benchmark derived from survey results of experts in the field of material selection.
The MixEval consists of two files: `CleanResponses` and `AllResponses`. Below presents the dataset file tree:
```
MSEval
│
├── AllResponses.csv
└── CleanResponses.csv
└── KeyQuestions.csv
```
# Dataset Usage
An example use of the dataset using the datasets library is shown in https://github.com/cmudrc/MSEval
To use this dataset using pandas:
```
import pandas as pd
df = pd.read_csv("hf://datasets/cmudrc/Material_Selection_Eval/AllResponses.csv")
```
Replace AllResponses with CleanResponses and KeyQuestions in the pathname if required.
# Citation
If you found the dataset useful, please cite:
```bibtex
@misc{jain2024msevaldatasetmaterialselection,
title={MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models},
author={Yash Patawari Jain and Daniele Grandi and Allin Groom and Brandon Cramer and Christopher McComb},
year={2024},
eprint={2407.09719},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.09719},
}
```
license: mit
---
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